ORIGINAL RESEARCH article
Front. Dent. Med.
Sec. Systems Integration
This article is part of the Research TopicShaping the Future of Dental Education: Transformative Strategies and Global PerspectivesView all 3 articles
Perceptions of Dental Students on the Integration of Artificial Intelligence in Radiology Clinical Education
Provisionally accepted
- 1University of Florida College of Dentistry, Gainesville, United States
- 2Department of Biostatistics and Bioinformatics, Computational Biology Institute, The George Washington University, Washington DC, United States
- 3The University of Jordan School of Dentistry, Amman, Jordan
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Objective: To assess dental students' perceptions of artificial intelligence (AI) in radiology education, focusing on diagnostic value, curriculum preparedness, and faculty support. Methods: An anonymous survey was administered to third-year dental students (n=66, response rate 71.7%) at the University of Florida College of Dentistry after exposure to the Overjet Caries Assist (OCA) platform (Overjet Inc. Claymont, DE, USA). Likert-scale, multiple-choice, and open-ended items captured attitudes toward diagnostic accuracy, skill development, curriculum integration, and patient communication. Descriptive statistics, polychoric correlations with bootstrap resampling, and thematic analysis of qualitative responses were conducted. Results: Most students reported that AI improved their ability to detect caries (89.4%) and enhanced radiographic interpretation (92.4%). However, only 16.7% agreed the curriculum adequately prepared them to use AI clinically, and just 45.5% felt confident about integrating AI into future practice. Open-ended feedback highlighted three themes: 1) need for structured faculty training, 2) earlier and more frequent AI exposure, and 3) emphasis on mitigating automation bias, or the over reliance on technology and automated systems in clinical judgement. Correlation analysis revealed strong associations between improved interpretation, skill development, and patient communication (r > 0.80), however, significant negative correlations emerged between student outcomes and perceptions of faculty preparedness. Conclusions: Students value AI as a diagnostic learning aid but identify gaps in curricular structure and faculty calibration. A structured, faculty-led AI curriculum introduced early in training and paired with patient communication strategies may optimize preparedness while safeguarding critical thinking.
Keywords: artificial intelligence, automation bias, curriculum development, dental education, Radiology
Received: 29 Oct 2025; Accepted: 03 Dec 2025.
Copyright: © 2025 Suri, Gohel, Alghamdi, Crowther, Garcia and Gohel. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ricky Amreek Suri
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.